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Noncausal autoregressions for economic time series [PDF]
, 2010 This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational ...
Lanne, Markku, Saikkonen, Pentti
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Bootstrapping periodically autoregressive models [PDF]
ESAIM: Probability and Statistics, 2017 The main objective of this paper is to establish the residual and the wild bootstrap procedures for periodically autoregressive models. We use the least squares estimators of model’s parameters and generate their bootstrap equivalents. We prove that the bootstrap procedures for causal periodic autoregressive time series with finite fourth moments are ...
Ciołek, Gabriela, Potorski, Paweł
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Improving Autoregressive NMT with Non-Autoregressive Model [PDF]
Proceedings of the First Workshop on Automatic Simultaneous Translation, 2020 Autoregressive neural machine translation (NMT) models are often used to teach non-autoregressive models via knowledge distillation. However, there are few studies on improving the quality of autoregressive translation (AT) using non-autoregressive translation (NAT).
Long Zhou, Jiajun Zhang, Chengqing Zong
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AR, CR and ARCR modeling for simulations and analyses of karst groundwater quality parameters [PDF]
Geološki Anali Balkanskoga Poluostrva, 2018 Although an invisible component of the hydrologic cycle, groundwater generally takes precedence over other water resources in the area of drinking water supply.
Ristić-Vakanjac Vesna +4 more
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Auxiliary Guided Autoregressive Variational Autoencoders [PDF]
, 2018 Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics
Lucas, Thomas, Verbeek, Jakob
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Autoregressive functions estimation in nonlinear bifurcating autoregressive models [PDF]
Statistical Inference for Stochastic Processes, 2016 Bifurcating autoregressive processes, which can be seen as an adaptation of au-toregressive processes for a binary tree structure, have been extensively studied during the last decade in a parametric context. In this work we do not specify any a priori form for the two autoregressive functions and we use nonparametric techniques.
Bitseki Penda, Siméon Valère +1 more
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Bayesian Model Selection for Beta Autoregressive Processes [PDF]
, 2010 We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the ...
Casarin, R., Leisen, F., Valle, L. Dalla
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IEEE Access, 2023 Lossless image compression is an important research field in image compression. Recently, learning-based lossless image compression methods achieved impressive performance compared with traditional lossless methods, such as WebP, JPEG2000, and FLIF.
Ran Wang +3 more
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Remote Sensing, 2021 High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability.
Ling Zhang +4 more
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Sensors, 2022 Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and ...
Giovanni Corvini, Silvia Conforto
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